Stephan J. Ihle

This paper introduces a reinforcement learning framework for controlling engineered neuronal networks to identify stimulation patterns that drive specific activity motifs.

The paper introduces inkube, an open-source system for culturing neurons, electrophysiology, and fluidic exchange, showing that environmental factors like temperature and medium evaporation significantly affect neuronal spiking behavior.

This paper develops an implantable biohybrid nerve model for synaptic deep brain stimulation, demonstrating in vitro and in vivo functionality for over 22 days.

This paper demonstrates in vitro neuronal networks capable of performing Boolean computations like NAND and OR using controlled topology and extracellular stimulation.

The paper introduces a modular and flexible open-source cell incubator system for both mobile and stationary use, enabling customized incubation for diverse experimental needs.

This study develops an in vitro retinothalamic nerve model using microfluidic channels to investigate signal transmission and network integrity.

This study investigates how microchannel width and spatial constraints affect axon growth in brain-on-chip systems, finding that restrictions smaller than 350nm prevent axon penetration.

This paper develops in vitro neuron-glia circuits using human iPSC-derived neurons and rat glia to test neuroactive compounds.

This paper describes a method to engineer biological neural networks on high-density CMOS microelectrode arrays to improve spatial resolution and axon guidance.

The paper presents a method to create topologically controlled circuits of human iPSC-derived neurons for long-term electrophysiology recordings.

This paper presents a dynamic causal model of a two-layered cortical microcircuit using magnetoencephalography to study non-invasive layered computations.